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Comprehensive B-Tagging Algorithms for CMS Physics

This document explores B-tagging algorithms used by CMS to identify b-quark jets crucial for top quark, Higgs, and SUSY analyses. It covers techniques like track counting, impact parameters, and secondary vertex tagging. By utilizing track properties, mass, and decay characteristics, CMS optimizes b-tagging for various physics analyses.

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Comprehensive B-Tagging Algorithms for CMS Physics

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  1. B-Tagging Algorithms for CMS Physics Gavril Giurgiu Johns Hopkins University on behalf of the CMS collaboration Flavor Physics and CP Violation 2009 Lake Placid, NY May 27 – June 1, 2009

  2. Introduction • B tagging algorithms attempt to identify (tag) jets produced by b-quarks • Identifying b-quarks is crucial for analyses of top quarks, Higgs, SUSY,… • Separate b-jets from light (u,d,s,g) jets using specific b-hadron properties: • - large lifetime ~1.5 ps (large decay length: 20 GeV b-hadron decays • after ~2 mm) • - search tracks or vertexes displaced w.r.t. primary vertex • - large mass ~5 GeV • - search leptons, from semileptonic B decays, with large • transverse momentum w.r.t. jet axis fragmentation track displaced track large impact parameter b-hadron PV displaced track jet axis large impact parameter lepton fragmentation track

  3. Impact Parameter • Large B hadron lifetime → large impact parameter,d0, of B decay products • Search for tracks displaced w.r.t. primary vertex • Loose track quality cuts → good fake b rejection • Use either 2D (transverse plane to beam line) • or 3D impact parameter • Use “sign” of the impact parameter: • positive if track intersection with jet • axis is downstream the PV along jet • direction • Impact parameter significance, • d0/sd0, used as discriminant between • signal (true b-jets) • and background (fake b-jets)

  4. Signed Impact Parameter • Different signed impact parameter distributions for bottom, charm or light jets • Jets with transverse momentum of 80 - 120 GeV • selected from generic QCD sample transverse (2D) three dimensional

  5. Track Counting B-Tagging • Use 3D signed impact parameter as discriminator • Require at least 2 (high efficiency) or 3 (high purity) tracks with • 3D sign IP larger than certain value • - Results on full simulation of tt decays with different alignment scenarios:

  6. Track Probability B-Tagging • Determine the probability that each track in jet comes from primary vertex • Estimate a combined probability that all tracks in jet come from PV • Use combined probability as discriminant b jets light jets c jets

  7. Secondary Vertex • Large B hadron lifetime → decay vertex (secondary • vertex) significantly displaced w.r.t. primary vertex • Search secondary vertex using all tracks in jet • Reject M > 6.5 GeV, remove Ks • Use decay distance significance • d/sd as discriminant

  8. Secondary Vertex B-Tagging • Search for secondary vertex • - use either 2D or 3D decay distance • use impact parameter and vertex mass as well • Combined above in likelihood discriminant

  9. “Soft” Leptons • Exploit large semileptonic branching fraction of B decays ( Br(b → lepton) ≈ 10% ) • along with the large b-hadron mass (~5 GeV) • - Leptons from b decays are characterized by: • large impact parameter w.r.t. PV • large transverse momentum w.r.t. jet axis (PTREL) • large angular distance w.r.t. jet axis • Lepton (muon or electron) quality selection • improves b-tagging purity

  10. “Soft” Leptons • Study both PTREL and signed IP as discriminants between gluon, u,s,d, • charm and b-jets • - Results with QCD and tt samples and full detector simulation: PTREL signed 3D IP

  11. “Soft” Leptons B-tagging • Search for muons and leptons in each jet • Combine discriminating variables in neural network to discriminate light from • heavy flavor jets • - Efficiency limitted by semileptonic b branching fraction

  12. Conclusions • Various B-tagging algorithms exploit characteristics of b hadrons: • lifetime, mass, semileptonic decays • Different algorithms are available at CMS • Use simple and more robust b-taggers at the beginning of data taking • - track counting, displaced vertex, lepton tagging • Use multivariate b-tagging techniques latter as data is better understood • to take full advantage of available information

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